The impact of county-level factors on meaningful use of electronic health records (EHRs) among primary care providers

This study examines the impact of county-level factors on “meaningful use” (MU) of electronic health records (EHRs) for 8415 primary care providers (PCPs) that enrolled in the Florida Medicaid EHR Incentive Program through adopting, improving, or upgrading (AIU) a certified EHR technology. PCPs received incentive payments at enrollment and if they used their EHRs in meaningful ways; ways that benefit patients and providers alike they received additional payments. We conducted a retrospective cohort study of these providers over the 2011–2018 period while linking their records to other state data. We used the core constructs of the resource dependence theory (RDT), a well-established organization theory in business management, to operationalize the county-level variables. These variables were rurality, poverty, educational attainment, managed care penetration, changes in population, and number of PCPs per capita. The unit of analysis was provider-years. For practical and computational purposes, all the county variables were dichotomized. We used analysis of variance (ANOVA) to test for differences in MU attestation rates across each county variable. Odds ratios and corresponding 95% confidence intervals were derived from pooled logistic regressions using generalized estimated equations (GEE) with the binomial family and logit link functions. Clustered standard errors were used. Approximately 42% of these providers attested to MU after receiving first-year incentives. Rurality and poverty were significantly associated with MU. To some degree, managed care penetration, change in population size, and number of PCPs per capita were also associated with MU. Policy makers and healthcare managers should not ignore the contribution of county-level factors in the diffusion of EHRs among physician practices. These county-level findings provide important insights about EHR diffusion in places where traditionally underserved populations live. This county-perspective is particularly important because of the potential for health IT to enable public health monitoring and population health management that might benefit individuals beyond the patients treated by the Medicaid providers.


Introduction
The diffusion of a technology encompasses both the decision to adopt a technology (inter-firm diffusion) and the extent at which adopting firms use the capabilities of the technology (intrafirm diffusion) [1].But studies on the diffusion of health information technology (health IT) among primary care providers (PCPs) typically focus on whether these providers adopt electronic health record (EHR) systems rather than on the use of these systems' capabilities across providers [2,3].There exist approximately 250,000 physician practices in the United States [4].They typically use a wide range of EHR systems, from basic to advanced EHR systems [3].The basic EHR systems replace the traditional medical charts and collect demographic and clinical data about the patient, including medical history, medications taken, and laboratory results [2,3].The advanced EHR systems incorporate computerized provider order entry (CPOE) capabilities that allow providers to electronically enter medical orders for patient medications and diagnostic tests [2,3].They also allow providers to write clinical notes and provide treatment guidelines [2,3].Hence, the advanced EHR systems can both complement and substitute labor for medical practices, involving clinical decision making and complex communications with pharmacies, laboratories, and the patients [5].This combination of technological and organizational capabilities is commonly referred to as "Meaningful Use" (MU) of EHRs [5].
In an effort to improve the quality and effectiveness of healthcare through the expanded use of EHRs in the United States, the Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the 2009 American Recovery and Reinvestment Act (ARRA), was signed into law [6].From 2011 to 2021, using funds available from the HITECH Act, the Centers for Medicare and Medicaid Services (CMS) funded the Medicaid EHR Incentive Program, later renamed the Medicaid Promoting Interoperability (MPI) program.In addition to adopting a certified EHR technology, this program required that PCPs use this technology in a "meaningful" way to ensure that health information is shared and exchanged to improve patient care [7].At the end of 2010, CMS issued the criteria relating to how eligible PCPs can demonstrate "meaningful use" of EHR technology to qualify for incentive payments [8].These criteria fall into four general categories: vocabulary, content exchange, privacy and security, and transport.More specifically, standards were set forth with regards to how PCPs should (a) record clinical summaries and prescriptions; (b) describe clinical procedures, laboratory tests, and medications; and (b) secure the transmission of information using the internet [8].These standards included fifteen "core set" objectives that all PCPs must achieve and 10 "menu set" objectives, of which PCPs must select 5 [8,9].Overall, the goals were to improve safety, quality, and efficiency of healthcare and reduce health disparities, engage patients and families in their care, improve population and public health, protect privacy and security of health information, and reduce healthcare costs [8,9].The MPI program was voluntary for state Medicaid agencies; but all the 50 states and the District of Columbia participated.The federal government covered about 90 percent of the cost [8].
Eligible providers included physicians, nurse practitioners, certified nurse-midwives, dentists, and physician assistants.To qualify they must meet a minimum of 30% Medicaid encounter volume threshold over a 90-day period, but pediatricians could qualify with a 20% Medicaid encounter volume.Additional requirements included being in active status, having no outstanding state or federal sanctions, and not being in a hospital setting.Providers must first register at the CMS-Registration and Attestations System (R&A).Once registered, they can apply online using the Medical Assistance Provider Incentive Repository (MAPIR) system.Each provider then received an incentive payment of $21,250 for committing to "adopt, implement or upgrade" (AIU) a certified EHR system.In practice, "adoption" consists of acquiring or purchasing a certified EHR system; "implementation" consists of installing or starting to use a certified EHR system; and "upgrading" consists of improving an existing EHR to a certified EHR system.Enrollment through AIU phases ended in 2016.
Providers received a second incentive payment of $8500 for attesting to MU; but pediatricians that enrolled with a 20-29% Medicaid encounter volume received $4250 [8,9].Payments for attesting to MU for the first time after AIU enrollment ended in 2018.Pre-payment controls were built into the MAPIR system to detect inaccuracies in eligibility, reporting, and payments.Provider audits that targeted suspicious and anomalous data and random audits were performed to ensure that incentive payments were made to providers that met all program requirements.Providers were not required to participate in subsequent or consecutive years.It is worth noting that eligible providers could also enroll into the MPI program simply by attesting to MU of EHRs in a 90-day reporting period, but these providers were excluded from this study.Further details on the program can be found elsewhere [8,9].
The present study used data from the Florida-Medicaid Promoting Interoperability (FL-MPI) program to examine the impact of county-level factors on MU attestation rates among PCPs after receiving the first-year incentive payments.Its contribution to the literature is significant.Overall, the broad question posed was: which environmental factors influence the widespread use of advanced EHR functions by PCPs?The corresponding policy implications are whether tools can be developed to overcome existing county-level disparities.Nearly 9 out of 10 physician practices in the U.S. have adopted a basic EHR system [10].But the rate of MU attestations has stalled among Medicaid providers, providing evidence of an emerging advanced use "digital divide", contrary to the conclusion reached for hospitals [10][11][12].Research indicates that the use of advanced EHR functions holds promise to address socioeconomic disparities for underserved populations [13,14].As health disparities are often geographically linked, it is important to obtain contextual insights about MU attestation rates among Medicaid providers.

Theoretical framework
The theoretical framework for this study is the resource dependence theory (RDT), a welldeveloped organization theory in business management [15][16][17].It offers relevant concepts to understand the diffusion of EHRs by identifying the importance of environmental factors [18][19][20].RDT dictates that providers will do what they can to secure resources from their environments through a variety of actions or exchanges to reach their goals of attesting to MU of EHRs [17].
The three core constructs of RDT are environmental munificence, dynamism, and complexity [17].Environmental munificence is concerned with the availability and accessibility of environmental resources [21,22].In this instance, it is tied to the availability of financial resources in the providers' markets as they seek financial resources in the forms of direct payments from the patients and reimbursements from public and private insurers [18].For example, providers in wealthier environments that cater to a more affluent and educated customer base might find it more beneficial to use the advanced functions of their EHR systems (patient portal; e-prescribe, etc.) to appeal to potential clients, typically more selective in their healthcare services [23].The second core construct, environmental dynamism, reflects the rate of change in the environments, which increases the providers' level of uncertainty [17,22,24].Providers in uncertain markets must adapt quickly to survive as the need to strategize to secure resources from these markets becomes more pressuring [22,24].The last core construct, environmental complexity, reflects the level of competition in the markets [22,25,26].In the face of competition, providers must compete for the scarce resources as well as secure their share of patients [18].For example, they might find it beneficial to adopt a health technology in areas of high competition as they need to appeal to the patient population [18,23].Although studies that employ RDT in examining healthcare providers are increasingly common, there exists little consensus on how to operationalize the market environment in the context of healthcare.With the exception of education, all the variables used for the RDT constructs in Fig 1 above were selected using a systematic review conducted by Yeager and colleagues who reviewed twenty studies that relied on aspects of RDT to examine the healthcare market [28].The authors summarized how the environment was measured in those studies and provided a list of variables that operationalized the RDT constructs [28].Educational attainment, typically used in economic studies as a proxy for human capital and skills available in a market [1,3], has also been used to operationalize environmental munificence in healthcare [27].The characteristics of the providers are not themselves part of RDT although they might enable or hinder the providers' ability to attest to MU [18,29,30].They are thus considered as controls in this study.

Data sources
The main database for this study was the Provider Participation Database for the FL-MPI program [31].It supplied data on each provider's unique 10-digit national provider identification (NPI), enrollment year, incentives payment amounts and dates, AIU phases, providers' specialties and zip codes, and practice types.The Florida Office of Insurance Regulation (FOIR) database provided data on managed care enrollments [32].Several measures came from the Area Health Resource Files (AHRF), a national database that provides current as well as historic data for more than 6,000 variables for each of the U.S. counties [33].The data source years included vary based on data availability but typically cover the previous 10 years.The AHRF data include information on healthcare professions, population characteristics, economics, healthcare utilization and expenditures, and health facilities.The basic file contains geographic codes and descriptors that enable it to be linked to many other files.We used the NPI and Florida county identifiers to link data from the above databases and construct the analysis sample.

Meaningful use (MU) attestations of EHRs
At enrollment, providers applied for the first-year incentive payment (Payment Year 1) and attested to one of the AIU phases as discussed earlier.They received a second payment (Payment Year 2) after attesting to meaningful use of EHRs.The outcome variable (MU) thus indicated whether a provider attested to MU during the study period.It was equal to 1 if a second payment was received and 0 otherwise.

County-level variables
We selected county variables that represented the three dimensions of the environment as shown in Fig 1 .A county's munificence was operationalized using three variables: rurality, poverty rate, and educational attainment [18,21,25].We categorized the counties as urban and rural (100 persons or less per square mile) using a Florida rurality map based on the 2010 census [34].Poverty rate was measured as the percentage of the county's population that lived in poverty defined as not having enough money to meet basic needs including food, clothing and shelter.Educational attainment was measured as the percentage of the county's population aged 25 years and older with a high school education.To represent environmental dynamism, we estimated the percentage of the county's population covered by a health maintenance organization (HMO) [32] and the percentage change in each county's population during the study period [19,35].Lastly, environmental complexity was measured by the number of nonfederal PCPs practicing in the county.It was expressed as the number of PCPs per 10,000 population.Except for the HMO enrollment data, all the county rates were estimated using data from the AHRF.

Control variables
The selection of the control variables was based on previous studies on EHR diffusion.Our analysis controlled for practices' characteristics, including providers' specialty and practice type.The literature indicates that provider's specialty is an important factor in optimizing the use of EHRs [30].Indicators for providers' specialty included being a physician, certified nurse midwife, nurse practitioner, dentist, pediatrician, or physician assistant.We used the NPI to obtain information on practice ownership, that is, whether the practice was an independent practice or part of a group practice.Practice type might affect the extent of use of EHR capabilities because of differences in the complexity of the decision structure and other human and economic performances [36,37].For example, group practices are more likely to have greater access to financial and managerial resources and may experience economies of scale by sharing resources such as administrative staff [27].We also controlled for EHR use at enrollment using AIU phase data to reflect the providers' sophistication with computer use [30,[38][39][40].We assumed that practices with a more enhanced EHR technology at enrollment might have a greater ability to use their EHR capabilities to achieve meaningful use of EHRs [38].Lastly, county and year dummies were included in the regression analysis.

Analytical approach
We conducted a 2011-2018 retrospective cohort study of 8415 providers that participated in the FL-MPI program.We narrowed our analysis to the counties that had a minimum of 10 providers to obtain enough grids for the county-level analysis [41,42].This reduced the number of counties from 67 to 53 counties.Because the dataset included only the dates the providers received payments for having attested to MU, it was not possible to determine the exact years of MU attestations.Hence, the unit of analysis was provider-years.Our sample included 2622, 2035, 1565, 777, 718, 698 providers that enrolled into the program, from 2011 to 2016, respectively.All the county ordinal variables were averaged over the study period.All data accessed complied with data protection regulation.
We calculated and presented descriptive statistics to characterize our sample.Next, we used one-way analysis of variance (ANOVA) to test for differences in MU attestation rates across each county variable.We then used pooled logistic regressions with generalized estimated equations (GEE) and the binomial family and logit link functions to examine the associations between MU attestations and the county-level variables, controlling for AIU phases and practices' characteristics [43][44][45].County and year dummies were also included.Using the delta methods, we estimated clustered standard errors to adjust for the non-independence of observations within counties [46][47][48].In view of the large number of explanatory variables, we calculated the vector of variance inflator factors (VIF) to check for possible multicollinearity.We found that the highest VIF was below 5 and the mean VIF was 2.21, indicating that correlations between the covariates were relatively acceptable and that multi-collinearity was not a serious problem [48].Odds ratios and corresponding 95% confidence intervals were derived using Stata 16 [49].

Results
Table 1 displays the descriptive statistics for the characteristics of the sample.The results indicate that, among the 8415 provider-years, physicians represented the largest group (52.1%), followed by nurse practitioners (23.6%), pediatricians (13.9%), dentists (8.1%), certified nursemidwives (1.9%) and physician assistants (0.4%).Group practices were more common (63.4%) than solo practices (36.6%).For the AIU phases, we found that 36.6% of the providers in the sample adopted a certified EHR system; 28.5% implemented a certified EHR system; and 34.9% upgraded to a certified EHR system.
Table 2 presents MU attestation rates for the overall program and by enrollment year.On the average, 41.9% of the sample achieved MU after receiving the first-year incentive.The result indicates notable decrease in MU attestation rates over time.More specifically, MU attestation rates decreased from 56.7% among the providers that enrolled in 2011 to 19.2% among those enrolled in 2016.
For practical and computational purposes, we categorized counties as poor counties (poverty rates �20%) and non-poor counties (poverty rates < 20%) [50].The other county rates were converted into three dichotomous variables corresponding to terciles (e.g., Q 1 -tercile group that includes counties with the lowest rates was referred to as "low group"; Q 3 -tercile group with the highest rates was the "high group"; and Q 2 -tercile group was the "medium group").The results for the bivariate analysis are presented in Table 3.They were all significant at p < .01,indicating that the MU attestation rates between the groups were not all equal.
Namely, rural counties, non-poor counties, counties with higher high-school graduation rates, counties with lower HMO penetration rates, counties with larger changes in population, and counties with a lower number of PCPs per capita had higher MU attestation rates.
Table 4 presents the multivariate results for the relationships between county variables and MU attestations.They indicate that providers located in rural counties were 80 percent more likely to attest to MU than providers in urban counties: OR = 1.80; 95% CI [1. 30 2.50].Providers located in poor counties were 32 percent less likely to attest to MU compared to providers in non-poor counties: OR = 0.68; 95% CI [0.55 0.84].Providers located in counties with high HMO penetration rates were 34 percent less likely to attest to MU than providers in counties with low HMO penetration rates: OR = 0.66, 95% CI [0.570.77].We also found that providers located in counties with moderate changes in their populations over the study period were 27% less likely to attest to MU compared to counties with lower changes in their populations: OR = 0.73, 95% CI [0.63 0.86].Finally, the results indicate that providers located in counties with a medium number of PCPs per capita were 45 percent more likely to attest to MU compared to providers located in counties with a lower number of PCPs per capita: OR = 1.46, 95% CI [1.24 1.74].
Most of the control variables (not shown on Table 4) were significant in the multivariate logistic regressions.More specifically, compared to physicians, pediatricians were more likely  to attest to MU while dentists and nurse practitioners were less likely to attest to MU. Group practices were more likely to attest to MU than solo practices.Finally, we found that providers that implemented or upgraded EHR systems were more likely to attest to MU than those that adopted EHR systems.
To check the robustness of the results, we use the approach used by Monestime et al. [30].We divided the participants into early participants and late participants, which were subsets of providers who enrolled in 2011-2012 and in 2013-2016, respectively.The results of the logistic regressions remained robust in all the models.

Discussion
While unexpedted, the findings that providers in rural counties had higher MU attestation rates than providers in urban counties were also found in more recent studies [51,52].A plausible explanation is the presence of regional extension centers (RECs) in the rural counties [51].The REC program was established by the HITECH Act and administered through the office of National Coordinator (ONC) for Health Information Technology.The program included 62 grantee organizations throughout the U.S. that promoted EHR adoption and the optimal use of EHR technology through outreach and the provision of technical assistance [53].Florida was among the 3 states with the highest number of RECs (4) granted by the ONC [53].The REC program prioritized small primary care practices in rural areas [53,54].Previous research showed that these providers had lower rates of EHR adoption than larger organizations and that they generally lacked resources to adopt and maintain EHRs [55].The REC program was found to be very successful in engaging providers in rural areas [6,51,56].
The findings that a county's poverty rate is negatively associated with MU of EHRs are consistent with those of most previous studies [57,58].They suggests that PCPs in these counties might face challenges to EHR diffusion related to tight operating budgets and limited capacity for integrating EHR training into the workflow [57].But it is to note that other researchers found mixed results [59,60].The results also indicate that HMO penetration rate was negatively associated MU attestations.While others found mixed results [61,62], this finding is consistent with another study conducted among a large number of physicians in Florida [63].It is also congruent with the medical technology literature indicating that higher managed care penetrations in geographically defined healthcare markets are associated with lower adoption of new technologies [61,64].We also found that moderate changes in a county's population size was associated with lower MU attestation rates.This finding is consistent with other studies indicating that uncertainty delayed the decision to pursue expensive strategies [22,65].Finally, we found that a medium or moderate increase in the number of physicians per capita (10,000) increased the probability of attesting to MU.This is in line with other studies in both healthcare and other industries that suggest that in the face of competition, organizations can adopt strategic decision making, become more innovative, and create competitive advantage [63,[66][67][68].Hence, in competitive markets, physicians might use their EHRs to brand their services as high quality and high technology to facilitate referrals [23].
Florida ranks among the states with the highest number of Medicaid recipients (over 6.7 million recipients as of 2023) [69].It is the third-largest state by population size (22.2 million in 2022) [70].Florida in 2022 was 77.05% White, 17.02% Black, and 5.93% other [70].Nearly half (45%) of working Floridians are living in or near poverty [69].As of 2022, 30 of the Florida's 67 counties were qualified as rural counties with about 750,000 residents [69].But more than 1 million persons live in the rural portions of Florida's 37 urban counties [69].Hence, Florida's varied socioeconomic status provides an ideal setting for contextual insights about meaningful use of EHRs among Medicaid providers that serve traditionally underserved communities.Meaningful use of EHRs requires EHR capabilities that include, among other things, patient portals, patient-monitoring system, and computerized provider order entry (CPOE) as discussed earlier.Leveraging advanced use, EHRs hold great promise to improve poor health outcomes and health equity [9,10,71].The Medicaid Promoting Interoperability Program has greatly contributed to the uptake of technology in clinical care practices in the U.S., as nearly 9 out of 10 offices have adopted a basic EHR system [10].However, the rate of MU of EHRs has stalled among Medicaid providers [10].In Florida specifically the majority of Medicaid providers do not use the EHR system to their full capabilities [30].Our study provides critical insights into the structural barriers (regional, local, economic, and resource-related issues) that hinder the optimal diffusion of health IT for low-resourced providers [71].
Our results might provide useful information to other initiatives to increase EHR diffusion in underserved communities.For example, the Health Resource and Services Administration is supporting several initiatives of EHR diffusion in community health centers and rural health clinics, which are important safety net providers of care for traditionally underserved populations [10].Our study can provide insights to these initiatives and the like across otherwise similar areas.These findings can also provide insights into the larger field of health informatics regarding factors that can influence an unintended "digital divide" for providers and healthcare institutions in medically underserved communities and regions [13].This study has limitations related to the datasets used.First, the data are from the state of Florida and, as a result, the generalizability to other states should be done with caution.Second, it was not possible to determine the exact year providers attested to MU.Hence, the sample dataset was constructed as a cross-section with multiple years of data rather than a longitudinal dataset.Nonetheless, the pooled logistic regressions with GEE have been shown to provide consistent estimates of effects similar to those obtained with the use of time-dependent Cox analyses [72,73].

Conclusion
The findings from this study may provide timely information on the merits of optimizing technology in low-resourced practice settings.Medicaid beneficiaries are traditionally low-income and vulnerable individuals with multiple comorbidities [74].While many feared that disparities could be exacerbated by the use of advanced EHR functions in the presence of existing "digital divide ", meaningful use of EHRs can yield health benefits for economically disadvantaged communities that often are prone to experience non-optimal health outcomes [75].Policies regarding increasing adoption and meaningful use of EHRs have typically focused on providing financial support to providers.While this is an important factor for many providers, particularly Medicaid providers, the findings for this study suggest that policy makers and healthcare providers should not ignore the contribution of county-level factors in the diffusion of EHR technologies among physician practices.Indeed, these findings provide important insights about EHR diffusion in places where traditionally underserved populations live.But this county-perspective is particularly important because of the potential for health IT to enable public health monitoring and population health management that might benefit individuals beyond the patients treated by the Medicaid providers [76,77].
This study is consistent with the Healthy People 2030 Health IT objectives of the ONC to increase the proportion of physician practices that have necessary information electronically available at the point of care, including those in geographically disadvantaged areas [10].It is also consistent with the ONC's national priority that the benefits of meaningful use of EHRs accrue to all patient populations as disparities in home broadband service by geographic location continue to persist [10,78].Research is needed to further investigate geographic

Fig 1
depicts the theoretical model of this study.It is adapted from Kissam et al.[27] and Menachemi et al.[19] and incorporates the variables that characterize the core constructs of RDT.